{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bank Marketing Data from Kaggle: Implementing and Experimenting with Artificial Neural Network\n",
    "\n",
    "### About the Problem: \n",
    "### Objective is to find the best strategies to improve for the next marketing campaign. How can the financial institution have a greater effectiveness for future marketing campaigns? In order to answer this, we have to analyze the last marketing campaign the bank performed and identify the patterns that will help us find conclusions in order to develop future strategies.\n",
    "\n",
    "\n",
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the 'bank' Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank=pd.read_csv('bank-full.csv',sep=';')\n",
    "df_Bank.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for Missing Value\n",
    "\n",
    "df_Bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation plot to pick out the best features for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Heatmap of Variables')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation:\n",
    "\n",
    "fig = plt.figure(figsize=(28,20))\n",
    "\n",
    "axis = sns.heatmap(df_Bank.corr(), cmap= 'YlOrRd', linewidth=1, linecolor='white', annot=True)\n",
    "axis.set_title('Heatmap of Variables', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the feature selection we are implementing the elimination using correlation backed up by the business intuition-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [corr]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df_Bank.corr().abs()\n",
    "\n",
    "#the matrix is symmetric so we need to extract upper triangle matrix without diagonal (k = 1)\n",
    "\n",
    "sol = (corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "                 .stack()\n",
    "                 .sort_values(ascending=False))\n",
    "sol = sol.to_frame()\n",
    "sol.columns=['corr']\n",
    "sol[sol['corr'] >  0.88]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=[\"job\", \"marital\", \"education\",\"contact\",\"month\", \"poutcome\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding for the above categorical features-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding job\n",
      "Encoding marital\n",
      "Encoding education\n",
      "Encoding contact\n",
      "Encoding month\n",
      "Encoding poutcome\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "for i in cat:\n",
    "    print(\"Encoding\",i)\n",
    "    df_Bank[i] = le.fit_transform(df_Bank[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup_nums = {\"default\":     {\"yes\": 1, \"no\": 0},\n",
    "                \"housing\": {\"yes\": 1, \"no\": 0}, \"loan\":{\"yes\": 1, \"no\": 0}, \"y\":{\"yes\": 1, \"no\": 0}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Bank.replace(cleanup_nums, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoded data looks like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   58    4        1          2        0     2143        1     0        2   \n",
       "1   44    9        2          1        0       29        1     0        2   \n",
       "2   33    2        1          1        0        2        1     1        2   \n",
       "3   47    1        1          3        0     1506        1     0        2   \n",
       "4   33   11        2          3        0        1        0     0        2   \n",
       "\n",
       "   day  month  duration  campaign  pdays  previous  poutcome  y  \n",
       "0    5      8       261         1     -1         0         3  0  \n",
       "1    5      8       151         1     -1         0         3  0  \n",
       "2    5      8        76         1     -1         0         3  0  \n",
       "3    5      8        92         1     -1         0         3  0  \n",
       "4    5      8       198         1     -1         0         3  0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Bank.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets have a overlook at the count of classes to check the Ratio of Classes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24f948989b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_Bank['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Its a clear case of \"Class Imbalance Problem\"-\n",
    "### Here the 'Class 0' which shows that \"Client is not Subscribing a term deposit\" accounts for more than 90% of the data and ' Class 1' accounts for merely 10%. But my objecive here is to identify the instances of 'Class 1'. I can reach an accuracy of around 90% by simply predicting 'Class 0' every time, but this provides a useless classifier for my intended use case. \n",
    "\n",
    "### Due to this reason,  I will 'undersample' the instances of majority class (i.e 'Class 0'). This way I will simply create a balanced data-set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Undersampling* data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5289"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes = len(df_Bank[df_Bank[\"y\"]==1])\n",
    "yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10578 entries, 33007 to 45208\n",
      "Data columns (total 17 columns):\n",
      "age          10578 non-null int64\n",
      "job          10578 non-null int64\n",
      "marital      10578 non-null int64\n",
      "education    10578 non-null int64\n",
      "default      10578 non-null int64\n",
      "balance      10578 non-null int64\n",
      "housing      10578 non-null int64\n",
      "loan         10578 non-null int64\n",
      "contact      10578 non-null int64\n",
      "day          10578 non-null int64\n",
      "month        10578 non-null int64\n",
      "duration     10578 non-null int64\n",
      "campaign     10578 non-null int64\n",
      "pdays        10578 non-null int64\n",
      "previous     10578 non-null int64\n",
      "poutcome     10578 non-null int64\n",
      "y            10578 non-null int64\n",
      "dtypes: int64(17)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "# Index of normal classes\n",
    "no=df_Bank[df_Bank[\"y\"]==0].index\n",
    "\n",
    "# Random sample non-fraud indexes\n",
    "no_ind=np.random.choice(no,yes,replace=False)\n",
    "\n",
    "# Index of fraud classes\n",
    "yes_ind=df_Bank[df_Bank.y==1].index\n",
    "\n",
    "# Concat fraud indexes and sample normal indexes\n",
    "final_ind=np.concatenate([no_ind,yes_ind])\n",
    "\n",
    "# Final balanced dataframe from undersampling\n",
    "balanced_df=df_Bank.loc[final_ind]\n",
    "\n",
    "balanced_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24f94ccddd8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(balanced_df['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of new balanced data-set after Undersampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10578, 17)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split: Splitting the data into Train and Test sets and scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x = balanced_df.drop(columns=['y'],axis=1)\n",
    "y = balanced_df['y']\n",
    "\n",
    "xTrain_org, xTest_org, yTrain, yTest = train_test_split(x,y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "xTrain = scaler.fit_transform(xTrain_org)\n",
    "xTest = scaler.transform(xTest_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xTrain Set (7404, 16)\n",
      "Shape of yTrain Set (7404,)\n",
      "\n",
      "Shape of xTest Set (3174, 16)\n",
      "Shape of yTest Set (3174,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of xTrain Set', xTrain.shape)\n",
    "print('Shape of yTrain Set', yTrain.shape)\n",
    "\n",
    "print('')\n",
    "\n",
    "print('Shape of xTest Set', xTest.shape)\n",
    "print('Shape of yTest Set', yTest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries for Neural Network and Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Creating a basic Neural Network model with just Input and Output Layer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "7404/7404 [==============================] - 0s 47us/step - loss: 0.6175 - acc: 0.6507\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x24f9a72acf8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Number of neurons in input layer = Number of features + 1(for bias)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "\n",
    "model.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 0s 12us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5313108413687015, 0.748784440874989]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3174/3174 [==============================] - 0s 10us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.536472662973494, 0.7309388784244516]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTest,yTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.72      0.72      1568\n",
      "          1       0.73      0.74      0.74      1606\n",
      "\n",
      "avg / total       0.73      0.73      0.73      3174\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1124  444]\n",
      " [ 410 1196]]\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(xTest)\n",
    "\n",
    "y_pred = np.where(y_test_pred>= 0.5, 1, 0)\n",
    "\n",
    "print('Classification Report:')\n",
    "print(classification_report(yTest,y_pred))\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(yTest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ---Experimenting with different Parameters (showing various Learning Curves also for better understanding)---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2a) Batch Size: Variation of Accuracy by Batch Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 0s 13us/step\n",
      "3174/3174 [==============================] - 0s 12us/step\n",
      "7404/7404 [==============================] - 0s 16us/step\n",
      "3174/3174 [==============================] - 0s 9us/step\n",
      "7404/7404 [==============================] - 0s 16us/step\n",
      "3174/3174 [==============================] - 0s 11us/step\n",
      "7404/7404 [==============================] - 0s 18us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n",
      "7404/7404 [==============================] - 0s 20us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n",
      "7404/7404 [==============================] - 0s 22us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n",
      "7404/7404 [==============================] - 0s 25us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "batch_size = [25,50,100,150,200,250,300]\n",
    "\n",
    "train_accuracy_array = []\n",
    "test_accuracy_array = []\n",
    "\n",
    "for i in batch_size:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    \n",
    "    model.fit(xTrain, yTrain, epochs=150, batch_size = i, verbose=0)\n",
    "    \n",
    "    train_accuracy_array.append(model.evaluate(xTrain, yTrain)[1])\n",
    "    test_accuracy_array.append(model.evaluate(xTest, yTest)[1])\n",
    "\n",
    "    \n",
    "x_axis = [25,50,100,150,200,250,300]\n",
    "plt.plot(x_axis, train_accuracy_array, c = 'g', label = 'Train Accuracy')\n",
    "plt.plot(x_axis, test_accuracy_array, c = 'b', label = 'Test Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Batch Size')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2b) Variation of Loss by Batch Size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 0s 25us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n",
      "7404/7404 [==============================] - 0s 27us/step\n",
      "3174/3174 [==============================] - 0s 9us/step\n",
      "7404/7404 [==============================] - 0s 29us/step\n",
      "3174/3174 [==============================] - 0s 11us/step\n",
      "7404/7404 [==============================] - 0s 31us/step\n",
      "3174/3174 [==============================] - 0s 9us/step\n",
      "7404/7404 [==============================] - 0s 31us/step\n",
      "3174/3174 [==============================] - 0s 10us/step\n",
      "7404/7404 [==============================] - 0s 34us/step\n",
      "3174/3174 [==============================] - 0s 11us/step\n",
      "7404/7404 [==============================] - 0s 37us/step\n",
      "3174/3174 [==============================] - 0s 12us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "batch_size = [25,50,100,150,200,250,300]\n",
    "\n",
    "train_loss_array =[]\n",
    "test_loss_array =[]\n",
    "\n",
    "for i in batch_size:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=150,batch_size=i,verbose=0)\n",
    "    \n",
    "    train_loss_array.append(model.evaluate(xTrain, yTrain)[0])\n",
    "    test_loss_array.append(model.evaluate(xTest, yTest)[0])\n",
    "    \n",
    "x_axis = [25,50,100,150,200,250,300]\n",
    "plt.plot(x_axis, train_loss_array, c = 'r', label = 'Train Loss')\n",
    "plt.plot(x_axis, test_loss_array, c = 'y', label = 'Test Loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Batch Size')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above experiment shows that for the \"batch_size = 150\" both Loss  is less and Accuracy are optimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3a) Epochs: Variation of Accuracy by Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 110us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 102us/step\n",
      "3174/3174 [==============================] - 0s 17us/step\n",
      "7404/7404 [==============================] - 1s 114us/step\n",
      "3174/3174 [==============================] - 0s 20us/step\n",
      "7404/7404 [==============================] - 1s 126us/step\n",
      "3174/3174 [==============================] - 0s 18us/step\n",
      "7404/7404 [==============================] - 1s 114us/step\n",
      "3174/3174 [==============================] - 0s 18us/step\n",
      "7404/7404 [==============================] - 1s 122us/step\n",
      "3174/3174 [==============================] - 0s 19us/step\n",
      "7404/7404 [==============================] - 1s 122us/step\n",
      "3174/3174 [==============================] - 0s 18us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "epochs = [25,50,100,150,200,250,300]\n",
    "\n",
    "train_accuracy_array = []\n",
    "test_accuracy_array = []\n",
    "\n",
    "for i in epochs:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=i,batch_size=150,verbose=0)                                    #batch size=150\n",
    "    \n",
    "    train_accuracy_array.append(model.evaluate(xTrain, yTrain)[1])\n",
    "    test_accuracy_array.append(model.evaluate(xTest, yTest)[1])\n",
    "\n",
    "    \n",
    "x_axis = [25,50,100,150,200,250,300]\n",
    "plt.plot(x_axis, train_accuracy_array, c = 'g', label = 'Train Accuracy')\n",
    "plt.plot(x_axis, test_accuracy_array, c = 'b', label = 'Test Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Epochs')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b) Variation of Loss by Epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 124us/step\n",
      "3174/3174 [==============================] - 0s 19us/step\n",
      "7404/7404 [==============================] - 1s 129us/step\n",
      "3174/3174 [==============================] - 0s 18us/step\n",
      "7404/7404 [==============================] - 1s 135us/step\n",
      "3174/3174 [==============================] - 0s 19us/step\n",
      "7404/7404 [==============================] - 1s 132us/step\n",
      "3174/3174 [==============================] - 0s 22us/step \n",
      "7404/7404 [==============================] - 1s 130us/step\n",
      "3174/3174 [==============================] - 0s 19us/step\n",
      "7404/7404 [==============================] - 1s 131us/step\n",
      "3174/3174 [==============================] - 0s 19us/step\n",
      "7404/7404 [==============================] - 1s 178us/step\n",
      "3174/3174 [==============================] - 0s 29us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "epochs = [25,50,100,150,200,250,300]\n",
    "\n",
    "train_loss_array =[]\n",
    "test_loss_array =[]\n",
    "\n",
    "for i in batch_size:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=i,batch_size=150,verbose=0)                                 #batch size = 150\n",
    "    \n",
    "    train_loss_array.append(model.evaluate(xTrain, yTrain)[0])\n",
    "    test_loss_array.append(model.evaluate(xTest, yTest)[0])\n",
    "    \n",
    "x_axis = [25,50,100,150,200,250,300]\n",
    "plt.plot(x_axis, train_loss_array, c = 'r', label = 'Train Loss')\n",
    "plt.plot(x_axis, test_loss_array, c = 'y', label = 'Test Loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above experiment shows that from epoch 150 to 200 Test Accuracy shows the incremental trend at  \"epoch = 200\" it has highest value, similarly with 200 epochs value of loss is also low. So I will choose 200 Epochs for my experiment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4a) Optimizer: Variation of Accuracy by Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 133us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 134us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 145us/step\n",
      "3174/3174 [==============================] - 0s 22us/step\n",
      "7404/7404 [==============================] - 1s 139us/step\n",
      "3174/3174 [==============================] - 0s 20us/step\n",
      "7404/7404 [==============================] - 1s 166us/step\n",
      "3174/3174 [==============================] - 0s 28us/step\n",
      "7404/7404 [==============================] - 1s 146us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 152us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "optimizer =['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "\n",
    "train_accuracy_array = []\n",
    "test_accuracy_array = []\n",
    "\n",
    "for i in optimizer:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = i, metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)                              # batch size = 150, epochs=200\n",
    "    \n",
    "    train_accuracy_array.append(model.evaluate(xTrain, yTrain)[1])\n",
    "    test_accuracy_array.append(model.evaluate(xTest, yTest)[1])\n",
    "\n",
    "    \n",
    "x_axis = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "plt.plot(x_axis, train_accuracy_array, c = 'g', label = 'Train Accuracy')\n",
    "plt.plot(x_axis, test_accuracy_array, c = 'b', label = 'Test Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Optimizer')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4b) Variation of Loss by Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 148us/step\n",
      "3174/3174 [==============================] - 0s 22us/step\n",
      "7404/7404 [==============================] - 1s 151us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 150us/step\n",
      "3174/3174 [==============================] - 0s 25us/step\n",
      "7404/7404 [==============================] - 1s 158us/step\n",
      "3174/3174 [==============================] - 0s 21us/step\n",
      "7404/7404 [==============================] - 1s 156us/step\n",
      "3174/3174 [==============================] - 0s 20us/step\n",
      "7404/7404 [==============================] - 1s 193us/step\n",
      "3174/3174 [==============================] - 0s 38us/step\n",
      "7404/7404 [==============================] - 1s 164us/step\n",
      "3174/3174 [==============================] - 0s 22us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "optimizer =['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "\n",
    "train_loss_array =[]\n",
    "test_loss_array =[]\n",
    "\n",
    "for i in optimizer:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = 'relu'))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = i, metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)                         # batch size = 150, epochs=200\n",
    "    \n",
    "    train_loss_array.append(model.evaluate(xTrain, yTrain)[0])\n",
    "    test_loss_array.append(model.evaluate(xTest, yTest)[0])\n",
    "    \n",
    "x_axis = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "plt.plot(x_axis, train_loss_array, c = 'r', label = 'Train Loss')\n",
    "plt.plot(x_axis, test_loss_array, c = 'y', label = 'Test Loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Optimizer')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Above experiment for Optimizer shows that for \"Adam\", Train and Test accuracy is high and loss is also low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5a) Input Activation Function: Variation of Accuracy by input activation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 153us/step\n",
      "3174/3174 [==============================] - 0s 23us/step\n",
      "7404/7404 [==============================] - 1s 156us/step\n",
      "3174/3174 [==============================] - 0s 20us/step\n",
      "7404/7404 [==============================] - 1s 165us/step\n",
      "3174/3174 [==============================] - 0s 23us/step\n",
      "7404/7404 [==============================] - 1s 169us/step\n",
      "3174/3174 [==============================] - 0s 24us/step\n",
      "7404/7404 [==============================] - 1s 170us/step\n",
      "3174/3174 [==============================] - 0s 23us/step\n",
      "7404/7404 [==============================] - 1s 174us/step\n",
      "3174/3174 [==============================] - 0s 22us/step\n",
      "7404/7404 [==============================] - 1s 188us/step\n",
      "3174/3174 [==============================] - 0s 26us/step\n",
      "7404/7404 [==============================] - 1s 183us/step\n",
      "3174/3174 [==============================] - 0s 24us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "activations = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "\n",
    "train_accuracy_array = []\n",
    "test_accuracy_array = []\n",
    "\n",
    "for i in activations:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = i))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)          \n",
    "                                                                                 # batch size=150, epochs=200, Optimizer=Adam\n",
    "    \n",
    "    train_accuracy_array.append(model.evaluate(xTrain, yTrain)[1])\n",
    "    test_accuracy_array.append(model.evaluate(xTest, yTest)[1])\n",
    "\n",
    "    \n",
    "x_axis = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "plt.plot(x_axis, train_accuracy_array, c = 'g', label = 'Train Accuracy')\n",
    "plt.plot(x_axis, test_accuracy_array, c = 'b', label = 'Test Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Input Activation Function')\n",
    "plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5b) Variation of Loss by input activation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 177us/step\n",
      "3174/3174 [==============================] - 0s 23us/step\n",
      "7404/7404 [==============================] - 1s 202us/step\n",
      "3174/3174 [==============================] - 0s 28us/step\n",
      "7404/7404 [==============================] - 1s 184us/step\n",
      "3174/3174 [==============================] - 0s 25us/step\n",
      "7404/7404 [==============================] - 1s 197us/step\n",
      "3174/3174 [==============================] - 0s 33us/step\n",
      "7404/7404 [==============================] - 1s 194us/step\n",
      "3174/3174 [==============================] - 0s 24us/step\n",
      "7404/7404 [==============================] - 1s 193us/step\n",
      "3174/3174 [==============================] - 0s 24us/step\n",
      "7404/7404 [==============================] - 1s 196us/step\n",
      "3174/3174 [==============================] - 0s 25us/step\n",
      "7404/7404 [==============================] - 1s 198us/step\n",
      "3174/3174 [==============================] - 0s 25us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,12)})\n",
    "\n",
    "activations = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "\n",
    "train_loss_array =[]\n",
    "test_loss_array =[]\n",
    "\n",
    "for i in activations:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(17, input_dim = 16, activation = i))\n",
    "    model.add(Dense(1, activation = 'sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "    model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)                 \n",
    "                                                                                 # batch size=150, epochs=200, Optimizer=Adam\n",
    "    \n",
    "    train_loss_array.append(model.evaluate(xTrain, yTrain)[0])\n",
    "    test_loss_array.append(model.evaluate(xTest, yTest)[0])\n",
    "    \n",
    "x_axis = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "plt.plot(x_axis, train_loss_array, c = 'r', label = 'Train Loss')\n",
    "plt.plot(x_axis, test_loss_array, c = 'y', label = 'Test Loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Input Activation Function')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So its clear from the above experiment that for \"softplus\" input activation function Accuracy is high and Loss is low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of above Experiments:\n",
    "\n",
    "### From all of the above experiments we got the following best parameters-\n",
    "### 1. batch_size = 150\n",
    "### 2. Epochs = 200\n",
    "### 3. Optimizer = Adam\n",
    "### 4. Activation Function = softplus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply above parameters on the basic Neural Network (with only i/p and o/p layer):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x24fe8768b70>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(17, input_dim = 16, activation = 'softplus'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "\n",
    "model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - 1s 190us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.377869840693242, 0.8326580227226389]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTrain, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3174/3174 [==============================] - 0s 31us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.40501939909557316, 0.8204158790170132]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTest, yTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.82      0.82      1568\n",
      "          1       0.82      0.83      0.83      1606\n",
      "\n",
      "avg / total       0.82      0.82      0.82      3174\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1280  288]\n",
      " [ 274 1332]]\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(xTest)\n",
    "y_pred = np.where(y_test_pred>= 0.5, 1, 0)\n",
    "\n",
    "print('Classification Report:')\n",
    "print(classification_report(yTest,y_pred))\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(yTest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: These values of Train and Test Accuracies (and loss) are clearly better than the previous NN model that we trained and test at the very beginning (where loss was around 0.52 and accuracy was around 0.71)\n",
    "### Based upon our observation of various learning curves, these experiments helped us to find the good parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Experiments:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Adding a Hidden Layer to see if Accuracy is improved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x24fec2e3d68>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(17, input_dim = 16, activation = 'softplus'))\n",
    "model.add(Dense(9, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])\n",
    "\n",
    "model.fit(xTrain,yTrain,epochs=200,batch_size=150,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7404/7404 [==============================] - ETA:  - 2s 220us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3562574301343683, 0.8423824959803374]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTrain, yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3174/3174 [==============================] - 0s 28us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.40938626616611073, 0.8229363579080026]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(xTest, yTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.82      0.82      1568\n",
      "          1       0.82      0.83      0.83      1606\n",
      "\n",
      "avg / total       0.82      0.82      0.82      3174\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1280  288]\n",
      " [ 274 1332]]\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(xTest)\n",
    "y_pred = np.where(y_test_pred>= 0.5, 1, 0)\n",
    "\n",
    "print('Classification Report:')\n",
    "print(classification_report(yTest,y_pred))\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(yTest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result: \n",
    "\n",
    "### A Term deposit is a deposit that a bank or a financial institurion offers with a fixed rate (often better than just opening deposit account) in which your money will be returned back at a specific maturity time.\n",
    "\n",
    "### Solution for next Marketing Campaign-\n",
    "### Banks should more focused on campaigns and customers with higher education, moreover, there should be some additional campaigning activities for the other cohort of users. A Questionaire or feedback forms can also be implemented in practice to understand the customer's views. Seasonality also plays a critical role here. Previous credit history of cleints can also be another deciding factor.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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